import torch.nn as nn
import torch
import math

__all__ = ['resnet50']


def resnet50(**kwargs):
    import torchvision
    model = torchvision.models.resnet50(**kwargs)
    return model

if __name__ == '__main__':
    net = resnet50()
    image = torch.randn(2,3,224,224)
    print(net)
    print(net.layer1[1].conv2)
    out = net(image)
    print(out.size())

    # print(distiller.weights_sparsity_summary(net))